Methods and systems for controlling body parts and devices using ipsilateral motor cortex and motor related cortex

ABSTRACT

A system for controlling a body part includes a number of sensing devices that sense signals from a hemisphere of a brain. A signal translating unit translates the signals into a command signal for controlling the body part, which is on a same side of the body as the hemisphere of the brain. A prosthetic device receives the command signal from the signal translating unit and manipulates the body part in response to the command signal.

RELATED APPLICATION DATA

This application is a continuation application claiming priority fromU.S. patent application Ser. No. 14/291,603 filed on May 30, 2014, whichclaims the benefit of U.S. patent application Ser. No. 12/133,919 filedon Jun. 5, 2008, which claims the benefit of U.S. ProvisionalApplication No. 60/933,433, filed on Jun. 5, 2007. Each of theseapplications is incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates, generally, to neuroprosthetics and, moreparticularly, to methods and systems for controlling body parts anddevices using ipsilateral motor cortex and motor related cortex.

BACKGROUND OF THE INVENTION

In normal brain function, one side of the brain (a hemisphere) controlsthe opposite side of the body. Thus, the right brain (right cerebralhemisphere) controls the left side of the body and the left brain (leftcerebral hemisphere) controls the right side of the body. As such, whenan individual has a stroke on one side of the brain, the opposite sideof the body is typically left paralyzed or weak.

This opposite side control of the body by the brain has dictated howconventional brain computer interfaces have been designed. Conventionalmethods and systems have used brain control devices that use signalsfrom the brain that correlate with contralateral arm movements (i.e.,decoding signals from the brain that control the arm and hand on theopposite side of the body) to control an external object such as arobotic arm. These methods have not used signals taken from a cerebralhemisphere (i.e., left) and used ipsilateral movements (i.e., left) as asignal for overt control.

Financial cost of lifetime care for U.S. subjects suffering fromhemispheric stroke is typically prohibitive. Hemiparesis is one of themost common reasons for their disability, and it is often hand functionthat is impaired. Motor cortex ipsilateral control to the affected limbis thought to play a role in recovery, yet its role in controllingipsilateral limb movement conventionally has not been well understood.Functional studies in both normal and stroke-recovered subjects havedemonstrated regions of activation with ipsilateral hand movements thatare distinct from those motor sites associated with contralateral handmovements. Conversely, some groups have found ipsilateral activation notto correlate, or worse, to be indicative of poorer outcome inhemiparetic patients or subjects. The conventional techniques used inthese studies, however, possess limitations of either spatial ortemporal resolution, prohibiting a more definitive understanding ofcortical processing of ipsilateral hand movements.

Therefore, there is a need to remedy the problems noted above and otherspreviously experienced for using signals taken from the same side of thebrain (ipsilateral) which correspond to movements from the same side ofthe body and to achieve an overt device control.

SUMMARY OF THE INVENTION

The foregoing problems are solved and a technical advance is achieved bymethods, systems and articles of manufacture consistent with the presentinvention, which provide neuroprosthetic controls of both sides of thebody by using a single brain hemisphere.

In accordance with methods consistent with the present invention, amethod for controlling a body part is provided. The method comprises:sensing a plurality of signals from a hemisphere of a brain; translatingthe sensed signals into a command signal for controlling the body part,which is on a same side of the body as the hemisphere of the brain; andmanipulating the body part in response to the command signal.

In accordance with systems consistent with the present invention, asystem for controlling a body part is provided. The system comprises: aplurality of sensing devices that sense signals from a hemisphere of abrain; a signal translating unit that translates the sensed signals intoa command signal for controlling the body part, which is on a same sideof the body as the hemisphere of the brain; and a device that receivesthe command signal from the signal unit and manipulates the body part inresponse to the command signal.

In accordance with articles of manufacture consistent with the presentinvention, there is provided a computer-readable medium containing acomputer program adapted to cause a data processing system to execute amethod for controlling a body part. The method comprises: sensing aplurality of signals from a hemisphere of a brain; translating thesensed signals into a command signal for controlling the body part,which is on a same side of the body as the hemisphere of the brain; andmanipulating the body part in response to the command signal. Thecomputer-readable medium may be, for example, a computer-readablestorage medium such as a solid-state memory, magnetic memory such as amagnetic disk, optical memory such as an optical disk, or acomputer-readable transmission medium, such as a modulated wave (such asradio frequency, audio frequency or optical frequency modulated waves)or a modulated downloadable bit stream that can be received by acomputer via a wired or a wireless connection.

Other features of the invention will become apparent to one with skillin the art upon examination of the following figures and detaileddescription. It is intended that all such additional systems, methods,features, and advantages be included within this description, be withinthe scope of the invention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an implementation of the presentinvention and, together with the description, serve to explain theadvantages and principles of the invention. In the drawings:

FIG. 1 is a block diagram illustrating one embodiment of a dataprocessing system that includes an electrocorticographic hemisphericbrain computer interface (BCI) used for bisomatic control in accordancewith the present invention;

FIG. 2A illustrates an embodiment of an electrode grid used on a subjectfor collecting cerebral signals in accordance with the presentinvention;

FIG. 2B illustrates the electrode grid of FIG. 2A placed over thesensorimotor cortex of the subject's head in accordance with the presentinvention;

FIG. 2C illustrates the subject connected to the BCI of FIG. 1 inaccordance with the present invention;

FIG. 2D is a schematic diagram of an embodiment of anelectrocorticographic (ECoG) BCI system in accordance with the presentinvention;

FIG. 3A illustrates two bar histograms in which a number of electrodessensing significant cortical activity are plotted against frequency foripsilateral and contralateral hand movements in accordance with thepresent invention;

FIG. 3B is a pie chart illustrating the number of anatomic locationsthat show significant changes in activity for ipsilateral andcontralateral hand movements in accordance with the present invention;

FIG. 4A shows images illustrating finger movements distinguished bydifferential cortical locations and frequency power alterations inaccordance with the present invention;

FIG. 4B is a table illustrating the number of identified fingers for acouple of subjects for both ipsilateral and contralateral motor actionsin accordance with the present invention;

FIG. 5A shows a bar histogram that illustrates peak times of signalcorrelation with the active condition averaged across three subjects inaccordance with the present invention;

FIG. 5B shows two graphs that data comparing the timing of the earliestsignificant electrode for contralateral movement and for ipsilateralmovement for a subject in accordance with the present invention;

FIG. 6 shows graphs illustrating activations superimposed onstereotactic brains of two subjects and the spectra associated withthose activation sites in accordance with the present invention;

FIG. 7A shows hemispheric differences in motor processing betweenstatistically significant electrode sites associated with ipsilateraland contralateral hand movements summated across four subjects inaccordance with the present invention;

FIG. 7B is a bar histogram illustrating a number of electrodes forhigh-frequency and low-frequency bands and their significance withrespect to ipsilateral or contralateral hand movements in accordancewith the present invention;

FIG. 8 is a table illustrating a comparison of accuracy of controlsachieved from signals derived from ipsilateral and contralateral motormovements in accordance with the present invention;

FIG. 9A is a graph illustrating performance curves that demonstrate theability of three subjects to utilize signals from sensorimotor cortexassociated with ipsilateral and contralateral hand movements to controla cursor on a computer screen in accordance with the present invention;

FIG. 9B is a graph illustrating tuning curves that demonstrate that foron-going controls the level of correlation between the control featureand the respective correct target in accordance with the presentinvention;

FIG. 10 shows two images that illustrate mapped activations when asubject performs contralateral and ipsilateral movements to move theleft hand and the right hand, respectively, in accordance with thepresent invention; and

FIG. 11 shows two images that illustrate mapped activations when asubject performs contralateral and ipsilateral movements to control acursor on a screen using brain signals alone in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to an implementation consistentwith the present invention as illustrated in the accompanying drawings.Wherever possible, the same reference numbers will be used throughoutthe drawings and the following description to refer to the same or likeparts. As would be understood to one of ordinary skill in the art,certain components or elements are not shown in the figures orspecifically noted herein to avoid obscuring the invention.

Conventional brain computer interfaces (BCIs) have typically offeredminimal benefit to subjects with motor impairment due to, for example,unilateral stroke because conventional platforms or systems rely onsignals derived from the contralateral motor cortex, which is the sameregion injured by the stroke or other impairment. For a BCI to assist ahemiparetic subject, the unaffected cortex ipsilateral to the affectedlimb (opposite the side of the stroke) needs to be utilized. Theaffected limb or body part may be motor-impaired due to, for example, aunilateral stroke, a spinal cord injury, a neuromuscular disorder, atraumatic brain injury, a limb amputation, and peripheral nerve injury.To do so, an expanded understanding of how motor cortex participates inprocessing ipsilateral limb movements is essential.

Methods, systems and articles of manufacture consistent with the presentinvention provide an implantable BCI that can control, for example, aparetic hand for the subject with a motor impairment, such as aunilateral stroke, by utilizing the cortical signals from the unaffectedhemisphere. This is achieved by identifying distinct and independentelectrophysiological features from, for example, the motor cortexassociated with ipsilateral hand movements and utilizing these featuresfor external device control and defining dynamic changes with ongoingperformance. The cortical electrophysiologic changes associated withipsilateral movements, such as hand movements, are distinct and theseunique ipsilateral changes can support an independent thought-drivendevice control. The cortical signals may be sensed, for example, fromone or more of the primary motor cortex, the premotor cortex, thefrontal lobe, the parietal lobe, the temporal lobe, and occipital lobeof the brain, and the like.

The cortical signals may be obtained from one or more ofelectrocorticographic (ECoG) signals, electroencephalography (EEG)signals, local field potentials, single neuron signals, (MEG)magnetoencephalography signals, mu rhythm signals, beta rhythm signals,low gamma rhythm signals, high gamma rhythm signals, and the like. TheECoG, EEG, local field potentials, and MEG signals may include at leastone of the following: mu rhythm signals, beta rhythm signals, low gammarhythm signals, and high gamma rhythm signals. The signal data isconverted into the frequency domain and spectral changes are identifiedwith regards to frequency, location, and timing. Features specific toipsilateral motor control, such as hand movements, may be utilized tocontrol a device, such as a cursor on a screen in real time (both inisolation and in parallel with contralateral hand tasks). This approachis innovative because it may capitalize on the high signal resolution ofECoG, for example, to reveal aspects of cortical motor processing notappreciable by noninvasive means.

FIG. 1 is a block diagram that depicts an embodiment of a dataprocessing system 102 consistent with the present invention. The dataprocessing system 102 includes a computing unit 104 configured toreceive sensor data or signals, translate or convert the received data,and communicate the translated data to control a device (not shown). Thecontrolled device may be any type of device that can be controlled by anexternal signal, such as but not limited to a robotic device, atransportation device, a prosthetic control device, and the like. In anillustrative example, the prosthetic control device may be an externalrobotic assist device. The prosthetic control device may utilize, forexample, one or more of external nerve stimulators, external musclestimulators, internally implanted nerve stimulators, and internallyimplanted muscle stimulators. The prosthetic control device may beutilized, for example, for hand controls, arm controls, leg controls,foot controls, bladder controls, and the like. In an illustrativeexample, the prosthetic control device may be a prosthetic limb for anamputee.

Computing unit 104 comprises a central processing unit (CPU) 106, aninput output I/O unit 108, a display device 110, a secondary storagedevice 112, and a memory 114. Computing unit 104 may further comprisestandard input devices such as a keyboard, a mouse, a digitizer, or aspeech processing unit (each not illustrated).

In the illustrative example, computing unit 104 communicates via anetwork 130, such as a LAN or the Internet, with a remote computing unit140. Remote computing unit 140 provides remote storage for computingunit 104. The number of computing units and the network configurationshown in FIG. 1 are merely illustrative. One of ordinary skill in theart will appreciate that the data processing system 102 may include adifferent number of computers and networks.

Memory 114 includes a program 120 having instructions for receivingsensor data, and converting the received data into controlling data tocontrol a device 150, such as a prosthetic device. Sensor data can bereceived from a variety of sources. In the illustrative example, sensordata is received from ECoG sensors 160, the prosthetic device 150, datagloves 162, a joystick 164, and a microphone 166. These devices aremerely illustrative. Additional or alternative devices may beimplemented. Data may be received via data interface devices 124, 126,128, and 129 and stored in a file 122 in the secondary storage 112. Inan embodiment, the data interface devices comprise Guger Technologiesoptically isolated g.USBamp amplifiers, or the like. AdTech medicalsplitter cables are used, for example, to connect to clinical monitoringcables.

The data gloves (1 right and 1 left) are, for example, 5DT 14 Ultra Datagloves. These gloves interface with the computing unit 104 via USBconnections and allow for direct measurements of finger movements to berecorded and used in data processing. These illustrative gloves have thecapability to measure finger flexion (2 sensors per finger) as well asfinger abduction. This information can be used to determine the timingof actual movements as well as their duration and velocity. These glovesare made of stretch Lycra that is well tolerated by users or subjectsand configured to fit many hand sizes.

The illustrative computing unit 104 is, for example, a Dell Precision690 with Quad Core Intel Xeon Processor X5355 (2.66 GHz, 4 MB RAM, 300GB storage). Further, the illustrative computer may be a mobile datacollection computing unit that may be moved to or with the subject.

The remote computing unit 140 is, for example, a Dell PowerEdge 2950server (Quad Core Intel Xeon E5345, 2.33 GHz, 1333 MHz, 16 GB RAM, 1.5TB Hard Drive with a Dell/EMC SAN Disk Enclosure). This computer set-upcan provide storage of large quantities of data. An average subject mayeasily generate 100 gigabytes of data.

The illustrative data interfaces are, for example, Guger technologiesg.USBamp Amplifiers. These FDA-approved amplifiers are opticallyisolated amplifiers that are approved for use with invasively monitoredsubjects. The optical isolation prevents electrical discharge from beingpassed from the computer system 102 to the subjects or users.Additionally, these amplifiers are compatible with BC1200 software. Eachamplifier is capable of recording 16 channels (i.e., 16 invasivelyplaced electrodes).

Forty percent of all stroke sufferers are left with a permanenthemiparesis; most commonly, this involves an acute decrement in handfunction that shows some recovery for several months. The undamagedhemisphere that is ipsilateral to the affected limb is thought to play arole in this stroke recovery. Relatedly, functional imaging studies havedemonstrated that motor cortex is involved in ipsilateral hand and limbmovements in both normal and stroke-recovered human subjects. Recentstudies suggest that sites associated with ipsilateral motor movementsare anatomically and temporally distinct from the locations and timingassociated with contralateral limb movements.

There are electrophysiologic features that distinguish and encodecortical processing for ipsilateral and contralateral movement, such ashand movements. One strategy in stroke rehabilitation is to aid theipsilateral cortex to take over function of the damaged contralateralhemisphere. Methods, systems, and articles of manufacture consistentwith the present invention accomplishing this through the use of thesystem's brain computer interface (BCI) that converts brain signalsdirectly to machine device commands without the need for the brain'snormal output pathways of peripheral nerves and muscles.

In another embodiment, the data processing system 102 is implemented asan implantable brain computer interface (BCI) that can control, forexample, a paretic hand for a subject with unilateral stroke byutilizing the cortical signals ipsilateral to the affected limb (i.e.,signals taken from the surface of the unaffected hemisphere). The BCIuses the cortical electrophysiologic changes associated with ipsilateralhand movements that are distinct and these unique ipsilateral changessupport independent thought-driven device control.

Ipsilateral hand and finger movements, for example, produceelectrocorticographic changes that have distinct cortical locations, areearlier in temporal onset, and associated with lower frequency spectralalterations when compared against contralateral hand movements.Localization of this effect is different between the right and lefthemisphere. The unique spatial and spectral electrophysiologic featuresassociated with ipsilateral hand movements can be effectively utilizedby a human subject to control an external device in accordance with thepresent invention. This is accomplished in isolation (ipsilateral handmovement alone), or in parallel with the physiologic operation of thecontralateral limb. With ongoing control, these brain signals willdemonstrate dynamic plasticity to improve performance.

The signals, such as ECoG signals, associated with ipsilateralmovements, such as hand movements, have anatomically distinct regions,occur earlier, and show lower frequency predominance when compared tocontralateral body part movements. These distinct signal features may beutilized, for example, to achieve control of an external device, such asa cursor on a computer screen.

The processing system 102 is configured to capitalize on the uniquespatial, temporal, and signal advantages of the signals, such as ECoG,to reveal aspects of cortical motor processing not possible bynoninvasive approaches. These distinct features are separable from thephysiologic changes associated with contralateral movements and can beutilized for external device control. These results provide asubstantive positive impact in that they provide neuroprostheticstrategies to ameliorate motor impairment, such as stroke-inducedhemiparesis. This alters conventional perceptions of stroke recoveryfrom one of watchful rehabilitation to a more directed approach ofrestoring function.

In an illustrative example, electrical activity taken directly from thesurface of the brain, or ECoG, provides a beneficial source forintegrated information that leads to a significant paradigm shift inunderstanding brain function compared to conventional approaches. ECoGhas a desirable signal-to-noise ratio, millisecond timescales,millimeter spatial resolution, and a broad frequency bandwidth that incombination are not available with other techniques. Throughexperimentation, the inventors have identified that ECoG is effective asa signal in motor brain mapping, neuroprosthetic applications, and itsability to convey very specific information regarding motor intentions.

The BCI consistent with the present invention does not depend on thebrain's normal output pathways of peripheral nerves and muscles. Theillustrative BCI decodes human intent from brain activity alone in orderto create an alternate communication and control channel for people withmotor impairments.

This brain-derived control is predicated on an understanding of corticalphysiology as it pertains to motor function. Research has determinedthat neurons in the motor cortex show directional tuning and, when takenas a population, can predict direction and speed of arm movements inmonkey models. Subsequently, these findings were translated tosubstantial levels of brain-derived control in monkey models andpreliminary human clinical trials. In another example of analyzingelectroencephalography (EEG), changes in amplitudes in sensorimotorrhythms associated with motor movement were described. As a result,these EEG signals have been used to achieve basic levels of control inhumans with amyotrophic lateral sclerosis (ALS) and spinal cord injury.

However, these conventional approaches do not assist subjects sufferingfrom hemispheric stroke. The conventional methods are based onfunctioning motor cortex capable of controlling the contralateral limbs.This situation does not exist in unilateral stroke. For a BCI to assista hemiparetic subject, the implant must utilize unaffected cortexipsilateral to the affected limb (opposite the side of the stroke). Todo so, an understanding of how the motor cortex participates inprocessing ipsilateral arm and hand movements must be used.

Conventional Approaches and Research

The notion that motor cortex plays a role in ipsilateral body movementswas determined when 15% of corticospinal neurons did not decussate incats. Further studies in single neuron recordings in monkey modelsextended this understanding to include ipsilateral hand and fingerfunction. For example, some studies demonstrated that a small percentageof primary motor cortical neurons showed increased activity withipsilateral hand movements. This primary motor cortical site was foundto be anatomically distinct from contralateral hand sites and, whenstimulated, produced ipsilateral hand movements. Additionally, a largersubset of premotor neurons was found to demonstrate more robustactivations with cues to initiate movement during both ipsilateral andcontralateral movements than with primary motor sites.

Additional findings demonstrated that in motor and supplemental motorcortex there was single neuronal activity associated with bilateralmovements that was distinct from unimanual movements. These findings ledto the conclusion that motor and motor-associated cortex share incontrol of both contralateral and ipsilateral limb and hand movements.

The evidence cited above has led to further investigation in humans.Clinical studies have demonstrated that injury to motor cortex still hasfunctional impact on the ipsilateral “unaffected” limb. Imaging studieswith functional magnetic resonance imaging (fMRI), positron emissiontomography (PET) and single photon emission computed tomography (SPECT),have further confirmed in normal human subjects that various levels ofipsilateral motor and motor-associated cortex are active withipsilateral hand movements. Other findings have extended this concept byshowing these regions to be anatomically distinct; located anterior,ventral, and lateral to the activations induced by contralateral handmovements.

Additionally, this activation appears to be more closely associated withhand movements that are more complex or lengthy in sequence duration.The hemispheric distribution has also been found to be asymmetric,favoring the left hemisphere in righthanded subjects. These findings ofdistinct anatomic position, association with increased manualcomplexity, and hemispheric dominance in normal human subjects have beenfurther corroborated by magnetoencephalography (MEG) and transcranialmagnetic stimulation (TMS).

The manner that motor cortex is involved with ipsilateral motormovements in humans, however, has not been well defined; moreover, theextant literature has conflicting findings. Utilizing fMRI, it wasdetermined that the time course analysis of complex ipsilateral fingermovements support the premise that primary motor cortex may participatein execution of complex movements rather than their planning. This,however, is in contradiction to findings which demonstrated ipsilateralpremotor areas having MEG dipole peak latencies that significantlypreceded contralateral M1 sensorimotor cortex in performing unilateralfinger movements. These findings were posited to support more of a motorplanning role in ipsilateral finger actions. Still another and oppositeperspective reported decreased fMRI bold signals in ipsilateral motorcortex with unilateral hand movements. This negative of baseline changeintensified with increased duration of movement. The authors postulatedthis to represent transcallosal inhibition. To date, it has not beenconventionally resolved whether these changing activations found onfunctional imaging or MEG represent motor planning, motor execution, orepiphenomenon related to transcallosal inhibition.

Definitive electrophysiologic studies in humans to parse out the rolethat motor cortex plays in ipsilateral hand movements and to define themanner in which it is physiologically encoded have been limited. This isdue either to the limitations of the modality or of the study design. Todate, the majority of conventional electrophysiologic studies of humanbrain function have utilized EEG. Brain activity has been assessed byeither alterations in field potentials or by the spectral changes ofoscillating brain activity (AKA sensorimotor rhythms). Ipsilateral handmovements have been shown to induce alteration in cortical potentialsprior to movement; this is referred to as “premotor positivity.”Spectral analyses of EEG signals have demonstrated bilateral lowfrequency responses with various finger and hand movements.Additionally, a more robust activation in left over right sensorimotorcortex in preparation and performance of simple finger movements wasdetermined. The EEG modality, however, is limited by poor spatialresolution (3 cm) and by spectral bandwidth (frequencies under 40 Hz).This ultimately limits the precision with which it can describe theanatomy and signal characteristics of the cortical electrophysiologyunderlying ipsilateral motor processing.

Ipsilateral Control of Devices

Unlike conventional approaches, methods, systems, and articles ofmanufacture consistent with the present invention provideneuroprosthetic controls of both sides of the body by using a singlebrain hemisphere. A plurality of signals is sensed from a hemisphere ofthe brain. In an illustrative example, Electrocorticography (ECoG), orsignal recorded from the surface of the brain is employed. The ECoGsignal is much more robust compared to EEG signal: its magnitude istypically five times larger, its spatial resolution as it relates toindependent signals is much greater (0.125 versus 3.0 cm for EEG), andits frequency bandwidth is significantly higher (0-500 Hz versus 0-40 Hzfor EEG). When analyzed on a functional level, different frequencybandwidths carry highly specific and anatomically focal informationabout cortical processing. The lower frequencies bands known as mufrequencies (8-12 Hz) and beta frequencies (18-26 Hz) may be produced bythalamocortical circuits and often decrease in amplitude in associationwith actual or imagined movements. Higher frequencies (>30 Hz), or gammarhythms, may be produced by smaller cortical assemblies and may beassociated with numerous aspects of speech and motor function. Noconventional studies or systems have utilized these ECoG spectralfeatures to analyze cortical processing of ipsilateral movements.

The same advantages in spatial and signal resolution that make the useof electrocorticography a superb method for brain mapping also confersimilar advantages for neuroprosthetic application. In experiments, thepresent inventors have demonstrated the first use of ECoG in closed-loopcontrol in one-dimensional and two-dimensional controls. Both wereaccomplished with minimal training requirements. Additional experimentsdemonstrated that specific frequency alterations encode very specificinformation about motor actions (e.g., direction of joystick movement).The present inventors further demonstrated that ECoG control usingsignals from the epidural space was also possible. Taken together, thesestudies show the ECoG signal to carry a high level of specific corticalinformation, and these signals can allow a user to gain control rapidlyand effectively.

Thus, the inventors have demonstrated that the corticalelectrophysiologic changes associated with ipsilateral movements, suchas hand movements, are distinct and that these unique ipsilateralchanges can support independent thought-driven device control. Throughexperimentation, the inventors arrived at these demonstrations buildingon initial studies that showed the following individual understandings:(1) there are distinct premotor anterior/lateral anatomic locationsfound in both animal models and in human functional imaging studiesassociated with ipsilateral hand and finger movements, (2) there isearlier temporal onset of brain signal alteration measured by the“premotor positivity” in EEG/ECoG and the anterior localized dipolemoments measured with MEG when compared to signals elicited bycontralateral hand movements, (3) there is a bilateral representation ofmu and beta rhythms with both real and imagined motor movements measuredwith EEG and ECoG, and (4) the high level of motor information and rapidand effective control that can be derived from the ECoG signal.

Methods and systems consistent with the present invention satisfy thesubstantial need to integrate the anatomic, temporal, and signal aspectsof the cortical physiology involved with processing ipsilateral handmovements and provide a utility for BCI application. This integration isaccomplished through the use of electrocorticography, for example. Thisallows for a BCI that achieves “bisomatic” control—a neuroprostheticthat can enable a single hemisphere to facilitate control of both sidesof the body.

Preliminary Studies

Through research, it has been determined that for both ipsilateral grosshand movements and finer hand movements (i.e., finger movements) thereare distinct anatomic sites of cortical activity that are more highlyrepresented in the lower frequencies. These findings underscore the highfidelity of ECoG at discerning information from cortex (from gross handmovements to individual finger movements) but also are important whenconsidering the type of hand prosthetic that may be used. Ipsilateralactivity occurs earlier than activity associated with contralateralmovements. These separable timescales support a more motor planning roleand further distinguish ipsilateral and contralateral processing. Thelow frequency spectra associated with ipsilateral movements conveysspecific information about the given motor movement. A differentanatomic localization exists between the right and left hemisphere foripsilateral motor processing. Beyond demonstrating that a distinctipsilateral cortical motor physiology exists, these features may beutilized to achieve independent real-time device control in time scalesthat make this approach feasible for translational application. Toutilize brain signals unique to ipsilateral hand movements for devicecontrol and to define dynamic changes with ongoing performance, it hasbeen demonstrated that a subset of subjects achieved control of acomputer cursor using signals derived from overt ipsilateral handmovements, and that improvement in performance are associated withongoing changes in brain signal. These findings show human subjectsgaining control substantially rapidly and, through ongoing feedback,they may alter their brain signals to optimize device performance.

The subjects in this study were six subjects (ages 11-50 years) withintractable epilepsy who underwent temporary placement of intracranialelectrode arrays to localize seizure foci prior to surgical resection.They included three men (Subjects 1, 2, and 3) and three women (Subjects4, 5, and 6). All subjects had normal levels of cognitive function. Twosubjects had right hemispheric 8×8 grid electrodes, two subjects hadleft hemispheric 8×8 grid electrodes, and two had bihemispheric stripelectrodes (1×8 electrode array). Each subject studied was in a sittingposition (semirecumbent), approximately 75 cm from a video screen (setupshown in FIG. 1). In all experiments, ECoG was recorded from up to 64electrodes from a combination of grids and strips using the generalpurpose BCI system BC12000 (Schalk, 2004). All electrodes werereferenced to an inactive intracranial electrode, amplified, bandpassfiltered (0.15-500 Hz), digitized at 1200 Hz, and stored. The amount ofdata obtained varied from subject to subject and depended on thesubject's physical state and willingness to continue. The subjectsperformed various hand, finger, and joystick tasks with their right andleft hands (described below). The time-series ECoG data was convertedinto the frequency domain using an autoregressive model. Spectralamplitudes were calculated between 0 and 200 Hz in 2-Hz bins. Thoseelectrodes and frequency bins with the most significant task-relatedamplitude changes (i.e., the highest values of r2) were identified. In asubset of subjects (3), closed-loop BCI experiments were attempted withthe subject receiving online feedback that consisted of one-dimensionalvertical cursor movement controlled by ECoG features that had showncorrelation with tasks during the various screening procedures.

FIGS. 2A-D show the illustrative system used during the preliminarystudy. FIG. 2A shows the 64-electrode grid that is 8×8 cm in size. FIG.2B shows an intraoperative picture of the grid placed over sensorimotorcortex. FIG. 2C shows a picture of the subject involved in the BCIoperation. Notable elements are the feedback screen in front of thesubject (*) and the BCI computer (**). FIG. 2D is a schematic diagram ofECoG BCI System. Once the subject had the subdural grid surgicallyimplanted for purposes of seizure monitoring, the ECoG signal was routedto the computer. This signal was then sent to the network for which thesignal tracings may be viewed for clinical purposes. For the purpose ofBCI operation, the signal is split directly from the subject (A). Thissignal is then sent to the BCI computer, where the raw signal wasanalyzed, stored, and used for online control. In this example, thedevice command is controlling the movement of a cursor on the feedbackscreen.

Ipsilateral hand and finger movements, for example, produceelectrocorticographic changes that have distinct cortical locations, areearlier in temporal onset, and associated with lower frequency spectralalterations when compared against contralateral hand movements.Localization of this effect is different between the right and lefthemisphere.

All subjects performed an ipsilateral and contralateral hand motor task.This consisted of the subject participating for a minimum of six minutesperforming repetitive three-second hand tasks consisting of opening andclosing the right or left hand on cue. Each hand task was interspersedby a rest period of equal time. The time series ECoG data was convertedinto the frequency domain and each hand action was compared againstrest. All subjects showed distinct electrodes sites and frequencyspectra that distinguished between the ipsilateral and contralateralhand movement. As can be seen from the data shown in FIGS. 3A and 3B,ipsilateral hand movements produced spectral power changes that arelower in frequency when compared against contralateral hand movementsand have anatomically distinct sites not present with contralateral handmovements.

FIG. 3A illustrates two bar histograms in which the number of electrodesdemonstrating significant cortical activity (spectral power changes withp-value<0.001) are plotted against frequency for ipsilateral andcontralateral hand movements. Ipsilateral hand movements arepredominantly represented in lower frequencies (average 32.8 Hz,SD+−14.4) compared to the higher frequency distribution associated withcontralateral hand movements (average 106.7 Hz, SD+−20.8). FIG. 3Bcompares the number of anatomic locations that showed significantchanges in activity (electrodes that show spectral power changes withp-values <0.001) for ipsilateral and contralateral hand movements. Thepie chart shows there are an equal number of cortical locations whichare distinct to ipsilateral and contralateral hand movements (eightsites each). Additionally, there are four sites that demonstrate anoverlap. In these sites ipsilateral and contralateral movementsdemonstrate different average frequency spectra (ipsilateral movements23.6 Hz, SD+−5.8 and contralateral movements 93.5 Hz, SD+1-24).Collectively, this data exhibits that there are distinct frequencyspectra and cortical sites that distinguish ipsilateral hand movementsfrom contralateral hand movements.

FIG. 3A shows that the number of electrodes that show significant powerchange (p-value<0.001) at a given frequency for ipsilateral andcontralateral hand movements across all subjects with intracranial gridarrays. Ipsilateral hand movements are represented in a lower frequencyrange than that associated with contralateral hand movements.

FIG. 3B illustrates the number of locations identified withstatistically significant power change (across all frequencies) thatcorrelated with ipsilateral and contralateral hand movements. The numberof sites that showed significant activity with ipsilateral handmovements (8) are in Area a, with contralateral hand movements (8) arein Area b, and locations that shared with both ipsilateral andcontralateral hand movements (4) are noted in Area c. This data showsthat there are sites for ipsilateral motor movements that are distinctfrom contralateral hand movements.

These figures represent data taken from all the subjects withintracranial grid arrays (Subjects 1 2, 3, and 6). The subjectsperformed three-second hand tasks consisting of opening and closing theright hand or the left hand on cue. Each hand task was interspersed by arest period of equal time. The timeseries ECoG data was converted intothe frequency domain using an autoregressive model in which each handaction was compared against rest. For each electrode, the amplitudechanges at each 5 Hz frequency bin were correlated with each hand taskby measuring the coefficient of determination values, or r2. An r2 valuegreater than 0.07 which has a p-value<0.001 is considered significant.Those electrodes found to be statistically significant were then plottedagainst frequency and also identified with regard to whether they weresignificant with ipsilateral or contralateral hand movements alone or incombination.

Distinguishing Individual Ipsilateral Finger Movements

To further define the level of resolution that electrocorticography candistinguish in the finer aspects of ipsilateral hand processing, namelyindividual finger movements, Subjects 1 and 2 were engaged to performindividual finger tasks consisting of tapping each individual finger oncue. The time-series ECoG data was converted into the frequency domainfor each finger movement and was compared against rest. From the resultsshown in the table of FIG. 4B, ipsilateral finger movements wereconsidered to be separable by the site of cortical activity and by theassociated frequency bands that show significant power changes withfinger movement. Additionally, for each subject the same finger ofeither hand could also be distinguished. Data from Subject 1 is shown inFIG. 4A, in which four feature plots are shown for contralateral indexand middle finger movement (top row) and ipsilateral index and middlefinger movement (bottom row). In each feature plot, frequency is plottedagainst anatomic location (electrode site). The shade change indicatesthe correlation of power change that occurs at that frequency bin withthe active task when compared against rest (measured in r2). The figureillustrates that the index and middle fingers are separable by thedistinct location and frequency power change for both the ipsilateraland contralateral conditions. Additionally, ipsilateral fingers can notonly be separated from the other complementary finger, but from allother fingers. Also of note, the ipsilateral finger movements, whencompared to their contralateral finger, have a significant portion oftheir unique spectral features in the lower frequencies (below 30 Hz).In this pilot study, 8 of 10 fingers were distinguished for the twosubjects tested. This data is summarized in FIG. 4B. These findingsdemonstrate that the methodology is able to achieve a high level ofresolution in distinguishing finer motor movements not discernable withother noninvasive modalities in humans. Moreover, the data processingsystem 102 is able to determine that the signals associated withipsilateral motor movements reflect specific manual actions (e.g.,finger movement) rather than just representing broad non-specificchanges.

The table in FIG. 4B is a summary of the data taken from Subjects 1 and2, who participated in cue initiated individual finger movements. Inboth subjects, eight of ten fingers were separable for a given hand andfrom all fingers from either hand. Ipsilateral and contralateral fingermovements which demonstrated significant power changes (p<0.001) wereidentified (third column). For a given hand, the significant electrodelocation patterns were compared to identify if those location patternsmatched with another finger movement induced electrode location pattern(ipsilateral index finger showed significant changes in electrodes 6, 7,10, 11, and 13 versus ipsilateral middle finger, which showed electrodeactivation in electrodes 1, 7, 13, and 14). If they did not, they wereconsidered separable for the given hand. The number of fingers separablefor the ipsilateral and contralateral hand is shown in the fourthcolumn. The electrode location patterns were then compared between hands(e.g., ipsilateral index finger versus all other nine fingers). If thegiven finger did not match any electrode location pattern of anotherfinger, it was considered separable. The number of fingers separablefrom all other fingers is shown in the fifth column.

FIG. 4A shows the distinguishing finger movements by differentialcortical locations and frequency power alterations. The data shows thatindex finger and middle finger movements demonstrate distinct locations(electrode, y axis) and frequency bands (x-axis) associated with thegiven finger, as indicated by circled areas in FIG. 4A. This isdemonstrated for both ipsilateral and contralateral finger movements.The same finger movement is different depending on whether it isipsilateral or contralateral. Additionally, ipsilateral finger movementshave a more predominant lower frequency representation than their samecontralateral finger movements. FIG. 4A represents data taken fromSubject 1. Subject 1 performed three-second finger tasks consisting oftapping each individual finger on cue. The finger tasks wereinterspersed by a rest period of equal time. The time-series ECoG datawas converted into the frequency domain using an autoregressive model inwhich each finger activity was compared against rest. The electrodeswere plotted against the frequency measured in 5 Hz Bins. The color wasscaled by the relative level of correlation that amplitude changeoccurred with the respective finger task (measured by coefficient ofdetermination values, or r2). An r2 value greater than 0.07 represents ap-value that is less than 0.001. The left column represents index fingermovements; the right column represents middle finger movements. The toprow indicates that these movements were contralateral, and the bottomrow indicates that these movements were ipsilateral.

Cortical Activity Occurs Earlier With Ipsilateral Hand Movements

To further define the unique aspects of ipsilateral motor processing,three subjects (1, 3, and 6) performed cue-directed hand-controlledjoystick center-out tasks with both the right hand and left hand. Thisarrangement allowed to precisely determine the timing of cuepresentation, motor movement, and associated spectra changes. From theresults shown in FIG. 5, the inventors concluded that ipsilateral handmovements are associated with earlier changes in the lower frequencyspectra than with contralateral hand movements. FIG. 5A presents a barhistogram that shows the peak time of signal correlation with the activecondition (time of cue presentation/movement against rest) averagedacross the three subjects. Ipsilateral movements preceded similarchanges with contralateral movements on average by 160 ms. FIG. 5B showsdata from Subject 6 comparing the timing of the earliest significantelectrode (activity vs. rest which had a p-value less than 0.001) forcontralateral movement and for ipsilateral movement (electrode locatedover Brodman's area 9 BA9, which is part of the frontal cortex in thehuman brain). The dotted line indicates the average time of initiationof movement onset. Here one can see that ipsilateral cortical activityprecedes movement while contralateral activity is after movement hasbegun. This activity is primarily in spectral changes below 30 Hz. Thisagain demonstrates that significant power alteration occurs prior tocontralateral hand movement and that this occurs in frequencies lessthan 30 Hz. These findings further demonstrate that both ipsilateral andcontralateral motor processing occur on different time scales andsupport the notion that motor cortex is involved in a more motorplanning role for ipsilateral hand movements.

As shown in FIGS. 5A and 5B, ipsilateral hand movements produce earlierchanges than contralateral movements. In FIG. 5A, the peak of signalcorrelation with the movement of a hand-operated joystick was averagedfor three subjects (1, 3, and 6). Ipsilateral hand movement precededcontralateral hand movement by 160 msec. In FIG. 5B, the data shows theprogression of power alteration in frequencies between 0.5 HZ to 60 Hzfor a significantly active electrode in Subject 6. The top figure is thesignificant power alteration associated with contralateral handmovement; the bottom figure shows the power change over time foripsilateral hand movement. Time zero is the cue for which Subject 6 wasinstructed to initiate movement with the joystick. The dotted line isthe initiation of movement. This data demonstrates that ipsilateralmovements induce low frequency changes that precedes onset of movementand spectral changes associated with contralateral hand movements (whichoccur at the onset and during movement).

With data taken from Subjects 1, 3, and 6, those electrodes thatdemonstrated a statistically significant (p-values less than 0.001)power change when movement was compared to rest were included. The timeperiod of 1000 ms after cue was presented was evaluated. The time ofpeak correlation of signal (at any frequency) with the active condition(measured with r2) was determined. FIG. 5B, in which bars representstandard deviation, illustrates data taken from an electrode over BA9from Subject 6. Subject 6 performed a hand-controlled joystick task inwhich she would direct a cursor to a target on the periphery of thescreen. This was performed using both the right hand and left hand. Thetime-series ECoG data was converted into the frequency domain using anautoregressive model. The spectrum was averaged for 1000 ms after cuefor movement was presented. The correlation of power change for therespective frequency band was measured by the coefficient ofdetermination, or r2 (r2 greater than 0.07 represents a p-value greaterthan 0.001, only significant spectral change shown). The dotted linerepresents the initiation of movement averaged from 80 trials.

Low-Frequency Spectra Encode Specific Motor Information

During experimentation, a hand-controlled force-feedback joystick taskwas utilized to further define the extent that the low frequenciesassociated with ipsilateral movements carry specific motor information.The task included a center-out task where the subject would direct andthen hold (against force) the joystick-controlled cursor at fixedpositions on targets at the periphery of the screen. The time-seriesECoG data was converted into the frequency domain for the entirejoystick task. The time that the cursor was held at upper and lowertarget positions was compared. Based on the results shown in FIG. 6,information specific to ipsilateral positional movements is more highlyrepresented in low-frequency spectra than are contralateral movements.Additionally, the brain sites where processing occurs are distinctbetween ipsilateral and contralateral movements.

FIG. 6 shows the data from two subjects (Subject 2 and Subject 6). Thetop row of FIG. 6 shows the sites (activations superimposed on astereotactic brain) and the significant spectra (p greater than 0.001)associated with those activation sites (adjacent bar histograms). Thetop row shows the sites associated with up/down motor positioning whenthe contralateral hand is utilized. The activation sites are verysimilar in location between Subjects 2 and 6 in premotor cortex. Thebottom row of FIG. 6 shows the sites associated with up/down motorpositioning when the ipsilateral hand is utilized. Here the locationsare inferior and anterior to the sites associated with contralateralhand control. The adjacent bar histograms show the number of electrodesfound to be significantly correlated in differentiating position for therespective 10 Hz frequency bin. When the contralateral frequencydistributions (top row) are compared to the ipsilateral frequencydistributions (bottom row), there is an increased representation oflower frequencies that are either not present with contralateralmovements (Subject 6) or at frequency bands distinct from those seen incontralateral processing (Subject 2). These findings demonstrate thatthe lower-frequency spectra convey significant information aboutspecific ipsilateral motor actions. Additionally, they show that thesites associated with ipsilateral and contralateral motor processing aredistinct.

As shown in FIG. 6, ipsilateral and contralateral motor processingoccurs at anatomically distinct sites with increased lower frequencyencoding for ipsilateral movements. The data shows the significantlocations on the brain where brain activity has been localized when theup position of a hand-controlled joystick is compared against the downposition. The adjacent bar graph plots the number of electrodes withfrequency bands that have significant correlation in distinguishingbetween the up and down position (p-value greater than 0.001). For bothSubjects 2 and 6 the location for contralateral processing is similar.The sites for ipsilateral processing are inferior. The frequenciesassociated with ipsilateral hand processing favor the lower frequencies,which are either not present with contralateral processing or atdifferent bands. The figure represents data taken from Subjects 2 and 6.The subjects performed center-out joystick movements in which they wouldhold the cursor on the target for a fixed period of time. Thetime-series ECoG data from the period that they held the cursor at thetop position and the bottom position was converted into the frequencydomain using an autoregressive model and were compared against eachother. The level of correlation of the signal oscillation for the upposition (versus down) was measured by the coefficient of determinationvalues, or r2. The data was summated across electrodes by placing aGaussian kernel (diameter 5 mm) that was centered on the stereotacticcoordinate of each electrode (derived from radiographs). The maximum ofthe kernel was determined by the respective r2 derived earlier andcentered at the electrode locus. This allowed locations of correlationto be plotted into stereotactically derived spaced and summated. Theadjacent bar graph is the number of electrodes plotted against 10 Hzfrequency bins that showed significant correlation (p-value greater than0.001).

Hemispheric Differences in Motor Processing

To define differences that may exist between hemispheres incontralateral and ipsilateral motor processing, data was summated fromfour Subjects (1, 2, 3, and 6) with homologously placed-grid arrays (tworight-sided grids and two left-sided grids) onto a single stereotacticbrain. Each of these subjects participated in right-hand and left-handtasks. This consisted of the subject performing a minimum of six minutesof repetitive three-second hand tasks consisting of opening and closingthe right hand or left hand on cue. Two specific frequency bands wereanalyzed: a low-frequency band (8-32 Hz) and a high-frequency band(75-100 Hz). Those electrode sites that showed spectral alteration inthe high or low frequency band with a p-value greater than 0.001 wereconsidered significant and plotted on the standardized brain. Theresults of this analysis are presented in FIG. 7B.

The electrodes that were significant (in either high or low frequency)and associated with ipsilateral hand movements are noted with circlesand those associated with contralateral hand movements, with squares.The bar histogram shows the number of significant electrodes for thehigh-frequency and low-frequency band and whether they were significantwith ipsilateral or contralateral hand movement. FIG. 7A illustratesthat there is a different spatial distribution for motor movements onthe right hemisphere and left hemisphere. The right hemisphere motoractions are more inferior to those of the left hemisphere. Additionally,ipsilateral movements have higher a proportion of significant electrodesassociated with lower frequencies than contralateral movements (whichare more highly represented in the higher frequencies). These findingsshow that the different hemispheres have a distinct localization foripsilateral motor processing and further confirm the low-frequencyrepresentation of ipsilateral hand movements.

FIGS. 7A and 7B show hemispheric differences in motor processing. InFIG. 7A, the data show the statistically significant electrode sitesassociated with ipsilateral (circles) and contralateral hand (squares)movements summated across four subjects (2 right/2 left) with subduralgrid electrodes arrays. These anatomic differences are different for thegiven hemisphere in that right-side ipsilateral sites area more inferiorthan left-sided ipsilateral sites. This data supports that there arehemispheric differences in the cortical localization of ipsilateral handmovements. In FIG. 7B, the bar histogram shows the number of significantelectrodes for the high-frequency and low-frequency bands and whetherthey were significant with ipsilateral or contralateral hand movement.The electrodes found to be significant with ipsilateral movement aremore highly represented in the low-frequency band (8-32 Hz), while thosefound to be significant with contralateral movement were in thehigh-frequency band (75-100 Hz).

Data was taken from the four subjects who had hemispheric subdural gridsplaced (Subjects 1, 2, 3, and 6). Each subject performed a three-secondhand task (opening and closing either right hand or left hand)interspersed by a rest period of equal time. All recorded ECoG data setswere referenced with respect to the common average. The time-series ECoGdata was converted into the frequency domain using an autoregressivemodel. For this plot, low and high frequency bands were chosen (8-32 Hzand 75-100 Hz, respectively). Those electrodes with 0.75 or greater ofthe r2 maxima (p-value greater than 0.001) were considered significant.Radiographs were used to identify the stereotactic coordinates of eachgrid electrode (Fox, 1985), and cortical areas were defined usingTalairach's Co-Planar Stereotaxic Atlas of the Human Brain (Talairach,1988) and a Talairach transformation database. The significantelectrodes were then plotted to a 3D cortical brain model from the AFNISUMA web site.

Utilizing Brain Signals Unique to Ipsilateral Hand Movements for DeviceControl and Defining Dynamic Changes with Ongoing Performance

The unique spectral and spatial electrophysiologic features associatedwith ipsilateral hand movements can be effectively utilized by a humansubject to control an external device. This can be accomplished inisolation (ipsilateral hand movement alone), or in parallel with thephysiologic operation of the contralateral limb. With ongoing control,these brain signals will demonstrate dynamic plasticity to improveperformance.

Achieving Online Control of a Cursor with Ipsilateral and ContralateralHand-Derived ECoG Signals.

To determine whether signals associated with ipsilateral hand movementscould be utilized, three of the six subjects (1, 5, and 6) who performedhand screening tasks (as described above) also were tested in areal-time online task to use features associated with either ipsilateralor contralateral overt hand movements to control a cursor on a computerscreen. The subjects received online feedback that consisted ofone-dimensional vertical cursor movement controlled by ECoG featuresthat had showed correlation with either the ipsilateral or contralateralhand movements during open-loop screening. The goal of the task was tohit one of two specified targets. Each subject achieved closed loopcontrol twice, once using a contralateral hand task and a second timeusing an ipsilateral hand task. Based on the data presented in FIG. 9and the table in FIG. 8, signals derived from ipsilateral motormovements can achieve high levels of control with final targetaccuracies between 70-96%.

This control is optimized when distinct locations and low-frequencyspectra associated with ipsilateral movements are utilized, which wasestablished in these three subjects by testing three different controlscenarios:

-   -   1) Ipsilateral features used for control were different from        contralateral features in both location and frequency spectra        (Subject 1),    -   2) Ipsilateral features were in the same location using a        high-frequency band (100 Hz) that overlapped for both        ipsilateral and contralateral control (Subject 5), and    -   3) Ipsilateral features in same location but using different        frequency spectra (ipsilateral—20 Hz, contralateral—100 Hz).

When low-frequency spectra was used for scenarios 1 and 3 (performancecurves 1 and 3), high levels of control were achieved with ipsilateralhand movements (91% and 96% accuracies). In scenario 2 (performancecurves 2), when overlapping high-frequency spectra (100 Hz) was used,the performance with ipsilateral hand movements was the worst with 70%target accuracy, while with contralateral movements a high level ofcontrol with 97% accuracy was still achieved. Scenario 2 (performancecurves 2) also demonstrated the most disparate learning curves showingthat high frequencies are less amenable to ipsilateral derived controlthan the lower frequencies. These preliminary findings by theinventors 1) were the first determination that ECoG signal derived fromipsilateral hand movements can be utilized for device control, and 2)they show that ipsilateral control signals can be differentiated fromcontralateral derived control features both in regards to corticallocation and frequency spectra.

To understand how the change in performance was accounted for duringonline control, the change of correlation (as measured by r2) of theECoG features, selected for control (specific frequency from specificelectrode) over time, was examined. From the results shown in FIG. 9B,with ongoing control, the level of correlation of the control feature tothe respective correct target increases. The progressive increase incorrelation reflects the subject's ability to alter their corticalphysiology with ongoing feedback. These changes occur over minutes andreflect a high level of cortical plasticity that can be induced by thismethodology. The level of correlation was highest with contralateraltasks utilizing high frequencies (100 Hz). Correlations of controlfeatures with ipsilateral hand movements were highest when low frequencyspectra (20-25 Hz) were utilized and lowest when high frequency spectra(100 Hz) were employed. These findings demonstrate the plastic nature ofhuman cortical physiology in adapting to device control and emphasizethe importance of lower-frequency spectra in their use for braincomputer interface applications associated with ipsilateral handprocessing.

FIGS. 9A and 9B show utilizing signals associated with ipsilateralmovements for external device control. FIG. 9A illustrates performancecurves. The data indicates the ability of three subjects to utilizesignals from sensorimotor cortex associated with either ipsilateral orcontralateral hand movements to control a cursor on a computer screen.Each subject is distinct in what features were chosen to utilize forcontrol:

-   -   Subject 1, different locations and different frequency spectra        (ipsi—25 Hz, contra 100 Hz) were used;    -   Subject 5, identical locations and spectra were utilized (both        utilized 100 Hz);    -   Subject 6, identical locations were used with different        frequency spectra (ipsi—20 Hz, contra—100 Hz).

These results demonstrate that optimal control can be achieved usingeither distinct locations or distinct frequency spectra. Performancewhen high frequency is utilized with ipsilateral hand movements is notas robust.

FIG. 9B illustrates tuning curves. The data shows the level ofcorrelation (as measured by r2) with the respectively chosen frequencyband utilized for control with the respective targets. Over time allsignals showed increased correlation demonstrating that these signalsexhibit plastic changes with ongoing feedback. The subjects receivedonline feedback that consisted of one-dimensional vertical cursormovement controlled by ECoG features that had showed correlation witheither the ipsilateral or contralateral hand movements during open loopscreening. For the ipsilateral limb and the contralateral limb therewere three-minute runs. Each trial began with the appearance of a targetthat occupies either the top half or the bottom half of the right edgeof the screen. One second later, the cursor appeared in the middle ofthe left edge of the screen and then moved steadily across the screenover a fixed period of 3.5 cm/sec with its vertical movement controlledcontinuously by the subject's ECoG features that were associated witheither ipsilateral or contralateral hand movement. The subject's goalwas to move the cursor vertically to the height of the target so that ithits the target when it reaches the right edge. The cursor movement wasvertically controlled every 40 ms by a translation algorithm based on aweighted, linear summation of the amplitudes in the identified frequencybands from the identified electrodes for the previous 280 ms.

These preliminary studies 1) demonstrated that ipsilateral handmovements are associated with distinct anatomic and temporal profileswhen compared to contralateral hand movements; 2) showed the corticalphysiology associated with ipsilateral hand movements conveys veryspecific information about motor actions; 3) demonstrated that encodingof specific motor movements have a higher representation in lowerfrequencies than contralateral hand movements; 4) provided strong cluesto different hemispheric localization in ipsilateral processing; 5)demonstrated for the first time that unique features associated withipsilateral hand movements can be utilized by a human subject foreffective device control; and 6) found that these control signals show ahigh level of plasticity in improving performance.

FIG. 10 shows two images showing a feature plot where channel plottedagainst frequency. The color change is significant power changes thatoccurred when the active condition is compared against rest. Thefeatures plot on the left is the activation that is mapped when thesubject who had a left hemispheric grid moved their right hand. Thefigure on the right is a features plot of when the subject with the sameleft hemispheric grid moved their left hand. As shown, the location andfrequencies are very different between the two actions. Thus thesedifferent signals potentially can thus be utilized to control thecontralateral arm naturally while using ipsilateral movements (real orimagined) to control something else in parallel.

FIG. 11 shows two images showing a feature plot where channel plottedagainst frequency. The color change is significant power changes thatoccurred when the active condition is compared against rest. Thefeatures plot on the left is the activation that is mapped when thesubject who had a left hemispheric grid moved their right hand tocontrol a cursor on the screen using brain signals alone. The figure onthe right is a features plot of when the subject with the same lefthemispheric grid moved their left hand to control a cursor on the screenusing brain signals alone. As shown, the location and frequencies arevery different between the two actions. This result shows that the samehemisphere can be utilized to accomplish bisomatic control—a singlehemisphere can control both the contra lateral side (as normal) and adevice to facilitate and assist their non functioning side (ranging fromsimple computer devices, to robotic exoskeletons, to implantedelectrodes in the body itself).

Methods, systems, and articles of manufacture consistent with thepresent invention could be commercially useful. For example, if anindividual can control both sides of their body with a single hemispherethis would have enormous implications for people with hemisphericstroke. Since 72% stroke subjects have strokes involving a single sideof their brain, developing a technology in which the healthy part oftheir brain can functionally compensate for the damaged portion couldhave significant impact.

Stroke is common. It is estimated 700,000 strokes occurred in the U.S.in 2002, 500,000 being first events and 200,000 recurrent strokes. Ifrates remain unchanged, it has been predict that 1,136,000 strokes willoccur in the year 2025, associated mainly with the aging of thepopulation. Though the majority of strokes occur in adult and elderlypopulations, it should be remembered that a significant number ofstrokes occur in children, particularly in the perinatal period. Strokeaccounts for 1 in every 15 deaths in the U.S. In the U.S. in 2003,stroke accounted for approximately 158,000 deaths directly, a figurewhich rises to 273,000 if deaths in which stroke was a contributorycause are included. Stroke is also the leading cause of disability inthe U.S. It has been estimated that in 2003 there were 5.5 millionstroke survivors in the U.S. population. The financial burden of strokeis substantial. It has been estimated that for the U.S., the direct andindirect cost of stroke in 2006 will be $57.9 billion. Approximately 72%of stokes involve one side of the brain.

While various embodiments of the present invention have been described,it will be apparent to those of skill in the art that many moreembodiments and implementations are possible that are within the scopeof this invention. Accordingly, the present invention is not to berestricted except in light of the attached claims and their equivalents.

What is claimed is:
 1. A system for assisting with movement of a bodypart affected by a stroke event, comprising: a brain signal acquisitionsystem comprising a plurality of sensing devices that sense electricalbrain signals from a brain of a subject having the body part affected bythe stroke event, the plurality of sensing devices including sensingdevices configured to sense electrical signals from a first hemisphereof the brain; a signal translating unit that translates the sensedelectrical brain signals from the first hemisphere of the brain into acommand signal for controlling the affected body part, wherein theaffected body part is on a same side of a body as the first hemisphereof the brain, wherein the signal translating unit further identifies,from sensed electrical brain signals acquired from the first hemisphereof the brain during a closed-loop brain computer interface control modeof operation, a subset of the sensed electrical brain signals having atleast one cortical feature associated with ipsilateral movement that isdistinct from a cortical feature associated with contralateral movement;and a device that receives the command signal from the signaltranslating unit and assists with moving the body part in response tothe command signal.
 2. The system of claim 1, wherein the plurality ofsensing devices sense electrical brain signals are selected from thegroup consisting of devices that sense electrocortigraphic (ECoG)signals, electroencephalography (EEG) signals, local field potentials,single neuron signals, (MEG) magnetoencephalography signals, mu rhythmsignals, beta rhythm signals, low gamma rhythm signals, and high gammarhythm signals.
 3. The system of claim 2, wherein ECoG, EEG, local fieldpotentials, and MEG signals include at least one of mu rhythm signals,beta rhythm signals, low gamma rhythm signals, and high gamma rhythmsignals.
 4. The system of claim 1, wherein the plurality of sensingdevices sense electrical brain signals from one of a primary motorcortex, a premotor cortex, a frontal lobe, a parietal lobe, a temporallobe, and an occipital lobe of the brain.
 5. The system of claim 1,wherein the device that receives the command signal from the signaltranslating unit and assists with moving the body part is command signalis communicated to one of a robotic device, a transportation device, anda prosthetic control device.
 6. The system of claim 1, wherein thedevice that receives the command signal from the signal translating unitand assists with moving the body part is an external robotic assistdevice.
 7. The system of claim 1, wherein the device that receives thecommand signal from the signal translating unit and assists with movingthe body part utilizes at least one of external nerve and musclestimulators.
 8. The system of claim 1, wherein the device that receivesthe command signal from the signal translating unit and assists withmoving the body part utilizes at least one of internally implanted nerveand muscle stimulators.
 9. The system of claim 1, wherein the body partcomprises an arm of the subject on the same side of the subject as thefirst hemisphere of the brain, wherein the first hemisphere of the brainis largely unaffected by the stroke event, and wherein the device thatassists in the movement of the body part assists in the movement of thearm.
 10. The system of claim 9, wherein the device that assists in themovement of the arm is a robotic exoskeleton.
 11. The system of claim 1,wherein the body part comprises a hand of the subject on the same sideof the subject as the first hemisphere of the brain, wherein the firsthemisphere of the brain is largely unaffected by the stroke event, andwherein the device that assists in the movement of the body part assistsin the movement of the hand.
 12. The system of claim 11, wherein thedevice that assists in the movement of the hand is a roboticexoskeleton.
 13. The system of claim 1, wherein the signal translatingunit converts the sensed electrical signals into a frequency domain andfurther determines spectral power changes for the sensed electricalsignals in the frequency domain.
 14. The system of claim 1, wherein theat least one cortical feature associated with ipsilateral movement isinitially identified during an open-loop screening process during whichthe subject performs actual or imagined movements of both (i)ipsilateral movements comprising movements of the affected body part onthe same side of the subject as the first hemisphere of the brain, and(ii) contralateral movements comprising movements of an unaffected bodypart on the opposite side of the subject from the first hemisphere ofthe brain.
 15. The system of claim 1, wherein the at least one corticalfeature associated with ipsilateral movement comprises spectral powerchanges being predominantly represented in a range of frequencies below75 Hz.
 16. The system of claim 15, wherein the cortical featureassociated with contralateral movement comprises spectral power changesbeing predominantly represented in a range of frequencies at or above 75Hz.
 17. The system of claim 1, wherein the at least one cortical featureassociated with ipsilateral movement comprises spectral power changesrepresented at an anterior/lateral location of the first hemisphere ofthe brain.